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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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Apple Leaf Disease Recognition Based on Improved Convolutional Neural Network with an Attention Mechanism 5

Apple Leaf Disease Recognition Based on Improved Convolution...

引用

5th International conference on Natural Language processing, ICNLP 2023

作者： Zhao, Guangyuan Huang, Xu Xi'an University of Posts and Telecommunications School of Automation Xi'an China

ISBN: (纸本)9798350302219

Traditional plant disease recognition algorithms have a complicated approach, difficult feature extraction, and low recognition accuracy. Based on the improved EfficientNetV2 model, this research classifies images of apple leaf disease. This study collected images of seven common apple leaf disease categories and one healthy category to address the present needs of various complex disease recognition scenarios. The disease images not only contain the common laboratory background but also add the background of the field growth environment of apple trees. And different recognition scenarios are further enriched by image enhancement techniques. For the model part, the processing of spatial feature information was strengthened while focusing on the channel feature information to ensure that the model focuses more on the subtle disease spot information for different disease classifications. The experimental results show that the accuracy of the model training recognition is 97.49%. To better evaluate this study, comparison experiments were conducted with five other popular convolutional neural network classification models, such as ResNet-50, DenseNet-121, Xception, MobileNet, and EfficientNet-B3. The improved models enhance the recognition accuracy of complex scenes and improve the model parameters and training speed. It provides a reference for apple leaf disease recognition and the development needs of smart agriculture. © 2023 IEEE.

关键词： Convolutional neural networks

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Lightweight Food image Recognition Algorithm Based on YOLOv8 12

Lightweight Food Image Recognition Algorithm Based on YOLOv8

引用

12th International conference on Information systems and Computing Technology

作者： Liang, Zaoqing Chen, Haoqi Ning, Chenfei Wang, Wenbao Fang, xiaomin South China Agr Univ Coll Math & Informat Guangzhou Peoples R China Guangzhou Nansha Mingzhu Agr Dev Co Ltd Guangzhou Peoples R China

ISBN: (纸本)9798350379860;9798350379877

In response to the current issues of large model parameters and slow recognition speed in food image recognition algorithms, this paper proposes an optimized and improved YOLOv8-GDC food image recognition model based on YOLOv8n. The model utilizes GhostModule (GM) Block to replace C2f, aiming to obtain sufficient feature representations with fewer computations, thereby reducing computational and parameter overhead. Additionally, the Convolutional Block Attention Module (CBAM) is integrated before the SPPF module in the backbone network. This mechanism dynamically adjusts the weights of feature maps in both spatial and channel dimensions, enhancing the model's focus on important features. Furthermore, one detection head for small targets is removed, and Dynamic Head (DyHead) is employed to strengthen the model's scale perception, spatial perception, and task perception using attention mechanisms, thus improving detection performance. Experimental results demonstrate that the improved model achieves an average precision mean (AP@ IOU=0.5) of 84% on the public food image dataset uec_test_ 81_110_dataset, which is a 3.0% improvement compared to YOLOv8n model. And it also reduces floating-point operations (GFLOPs) from 8.1 to 6.0, a 24.7% reduction compared to the baseline YOLOv8n model. The algorithm improves both the speed and accuracy of food image detection. It exhibits outstanding performance in detecting food images.

关键词： Food images Deep Learning Object Detection YOLOv8

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Reconstructing hyperspectral images from RGB images fused with multi-scale features

Reconstructing hyperspectral images from RGB images fused wi...

引用

2022 conference on Infrared, Millimeter, Terahertz Waves and Applications, IMT 2022

作者： Zhao, Juanning Lin, Song Zhao, Yi Sun, Lianshan School of Electronic Information and Artificial Intelligence Shaanxi University of Science & Technology Shaanxi Xi'an710021 China

ISBN: (纸本)9781510662476

The Hyperspectral image (HSI) can provide spectral information on the surface of objects, and has been widely used in remote sensing imaging, medicine and other fields. Due to the high cost of hyperspectral imaging systems, reconstruction of hyperspectral images from RGB images has become a research hotspot in recent years, but the reconstruction accuracy of existing algorithms is relatively limited. Based on the Transformer network, this paper designs an MC-Transformer model that integrates multi-channel feature extraction, uses a multi-channel encoder based on pixel scrubbing to extract spectral and spatial dimension features of different scales, through designs a multi-scale fusion strategy to fuse features, and finally analyzes high-dimensional features information and reconstruction hyperspectral images by decoder. Experimental results show that the MRAE of the reconstruction results of this method can reach 0.1795, which is 39.1% and 23.6% higher than the HRNet and AWAN models, respectively. © 2023 SPIE.

关键词： image reconstruction

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Underwater visual image denoising based on total variation method

Underwater visual image denoising based on total variation m...

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2022 conference on Infrared, Millimeter, Terahertz Waves and Applications, IMT 2022

作者： Li, Yangyang He, Dahua Li, Yapeng Huazhong Institute of Electro-Optics Wuhan National Laboratory for Optoelectronics Wuhan430223 China

ISBN: (纸本)9781510662476

Optical imaging technology is one of the main technology for underwater detection and recognition. However, the serious image noise occurred in the optical imaging and the signal to noise ratio of image decreased, due to water scattering and turbulence, background light interference and other factors. In this paper, an underwater visual image denoising algorithm based on total variation method is proposed to improve the signal-to-noise ratio of the underwater image at the same time keep the detail information of images. Based on the linear noise model of the image, the total variation model of the image is established and the partial differential equation of image problem is converted to optimization problems, which can be solved by gradient descent algorithm and finite difference algorithm. The method based on total variation was proposed to reduce the noise of underwater images compare with other traditional algorithms and verified by the simulation experiment and underwater experiment. Experimental verifications: after the image processing of the algorithm, the objective reference image quality evaluation index image peak signal-to-noise ratio (PSNR) was improved to more than 20 dB, and the score of no reference image quality evaluation index was improved by 4.8 times. The method can be applied to an underwater exploration and image enhancement for scatterer imaging applications. © 2023 SPIE.

关键词： Signal to noise ratio

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EgpuIP: An Embedded GPU Accelerated Library for image processing 24

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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24th IEEE International conference on High Performance Computing and Communications, 8th IEEE International conference on Data Science and systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Luhan Jia, Haipeng Zhang, Yunquan Li, Kun Wei, Cunyang Institute of Computing Technology Chinese Academy of Sciences State Key Lab of Processors Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research China

ISBN: (纸本)9798350319934

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm;2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm;3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm;4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to 19×88×, and 3× speedup respectively. © 2022 IEEE.

关键词： Graphics processing unit

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Chinese image Description Generation Model Based on Recurrent Fusion Encoding 5th

Chinese Image Description Generation Model Based on Recurren...

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5th International conference on 3D Imaging Technologies-Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Zhou, Shiyu Donghua University Shanghai China

ISBN: (纸本)9789819751839

The image description job is a crucial area of research in the nexus of computer vision and applications for natural language processing. The objective is to produce accurate text descriptions using the picture attributes retrieved by the model. The conventional encoder-decoder model produces an inadequate encoding feature because it does less direct dimensionality reduction on images and is deficient in sequence feature information. This research suggests a recurrent fusion encoding-based Chinese picture description generation model to solve the aforementioned issues. R-CNN is used to extract the image's features first. Then, at the encoding side, gated recurrent units and a soft attention mechanism are used to build a recurrent fusion network for full sequence fusion of the image features. Finally, the sequence feature information is combined with a self-attention mechanism for multi-level interaction before being sent to the encoding side. I carried out enough tests on the Flickr_8k dataset and the Ai_Challenger dataset to confirm the model’s validity. According to the experimental data, the model performs better than some Chinese image description algorithms in each index by 0.2–4.2% points, and the generated statements are more accurate and sufficient for image description. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Encoding (symbols)

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Secure Vision: Enhancing Data Security in Wireless Sensor Networks through image processing 8

Secure Vision: Enhancing Data Security in Wireless Sensor Ne...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Swamy, B. Venkata Gopi, S. Sudhakar, Kattupalli Girish, Pechetti Rani, M. Janaki Upadhyay, Shrikant B V Raju Institute of Technology Department of Chemistry Telangana State India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai Avadi India Lakireddy Bali Reddy College of Engineering Department of Ai & Ds Mylavaram India Bonam Venkata Chalamayya Institute of Technology and Science Batlapalem Amalapuram India Dr. Mgr Educational and Research Institute Department of Ece Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798350376425

The secure Wireless Sensor Network (WSN) architecture was designed in such a way that the tradeoffs among efficiency, scalability and security were balanced. It was consisted of several sensor nodes, cluster heads, base station as well as the control center all of them played a vital role in functioning and managing of data. This network security was provided with a layered security approach like the physical, data link, network, transport, and application layers. Data security and integrity are secured through advanced image processing algorithms, and reliable communication protocol. Regular maintenance, constant monitoring and hardening solutions through resilience and fault tolerance mechanisms added a layer of trust in securing this WSN. A comparative analysis was made using different algorithms, such as Intrusion Detection systems (IDS) and detection accuracy is 98% and proposed model which has shown a better result in the energy consumption, data transmission latency, encryption time, detection precision as well as efficiency to insure an original data integrity. © 2024 IEEE.

关键词： Data integrity

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Braille-to-Voice Converter Using image processing 2nd

Braille-to-Voice Converter Using Image Processing

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2nd International conference on Advances in Computational Intelligence and Informatics, ICACII 2023

作者： Prasad, P. Rajendra Ahmed, Roshan Zameer Lingamurthy, Chethan Raju Chaitra, A.M. Deepak, H.R. Ramaiah Institute of Technology Bangalore560054 India Presidency University Bangalore India

ISBN: (纸本)9789819747269

Approximately 2.2 billion people in the world are visually impaired and rely on assistance to meet their basic needs. Education is a fundamental human need and plays a crucial role in empowering individuals. For visually impaired individuals, education can not only integrate them into society but also enhance their professional and academic abilities. To facilitate better education for the visually impaired, specialized books, educational materials, and devices are necessary. Braille is a tactile system designed for people who are blind, enabling them to read and write through touch. Proposed work aims to improve the education of visually impaired individuals by developing a braille-to-voice converter. The system will use image processing techniques to capture an image of braille text, which will then be analyzed using computer vision algorithms to extract the text and convert it to speech. The ultimate goal is to create a cost-effective, accurate, and user-friendly tool that can be easily integrated into the daily lives of visually impaired individuals. With this system, visually impaired individuals will be able to easily and quickly convert braille text to speech in real time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Intelligent Frame and Table Segmentation in Blueprint Documents: Method and Implementation 6th

Intelligent Frame and Table Segmentation in Blueprint Docume...

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6th conference on Computational Methods in systems and Software (CoMeSySo)

作者： Hasan, Fudail Kashevnik, Alexey ITMO Univ St Petersburg Russia SPC RAS St Petersburg Russia Petrozavodsk State Univ PetrSU Petrozavodsk Russia

ISBN: (纸本)9783031214370;9783031214387

Blueprints are documents that contain the drawing of a design and the information that explains this design. Recently, the task to automatically recognize the information in the blueprint documents has become required. Segmenting the frame and tables in the blueprint is the first step of understanding the blueprint document. In this paper, we explain an automated method for frame and table segmentation. The proposed method processes the blueprint as an image and defines all the parts (pixels) of the blueprint that belongs to the frame or tables. It finds all the lines in the blueprint's image and decide which combinations of lines construct frame and tables. It can process blueprints that have high resolution. The proposed method allows to isolate frame and tables in the blueprint document. We achieved an accuracy of 99% for excellent quality documents.

关键词： Frame and table segmentation Blueprint processing Table detection Lines segmentation

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The Impact of Preprocessing on Face Recognition using Pseudorandom Pixel Placement 29

The Impact of Preprocessing on Face Recognition using Pseudo...

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29th International conference on systems, Signals and image processing (IWSSIP)

作者： Hadis, Muhammad Sabirin Akita, Junichi Toda, Masashi Nurzaenab Kanazawa Univ Dept Elect Engn & Comp Sci Kanazawa Japan Kumamoto Univ Ctr Management Informat Technol Kumamoto Japan STMIK AKBA Study Program Informat Engn Makassar Indonesia

ISBN: (数字)9781665495783

ISBN: (纸本)9781665495783

The condition of face images, data processing algorithms, and hardware capabilities can influence the accuracy of face recognition. Several studies have been conducted to increase performance of face recognition. One of the steps is to create or even improve the methods in preprocessing as one of the essential steps that can affect accuracy. This paper proposed a pseudorandom pixel placement method applied to the preprocessing step in face recognition to know the impact on accuracy. Eight face objects were used in this study. One face image for one object as training data was taken via a single-lens digital reflex camera and a smartphone. One video for one object was taken from Closed Circuit Television with two different placement angle conditions for testing data. The experiment was carried out with four variations of the basic resolution size of the face image in the testing data to see the performance of the proposed method. The result is five of eight face objects have improved accuracy than without using pseudorandom pixel placement. The best average accuracy result using the proposed method is 63.76% higher than without using the proposed method with a value of 60.09%, so preprocessing using the pseudorandom pixel placement on face recognition can increase accuracy.

关键词： Face Recognition Pseudorandom Pixel Placement One image Training Data image processing Computer Vision

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